On Design of Experiments and Quality by Design in the Pharmaceutical Industry

Transcription

1 VIP On Design of Experiments and Quality by Design in the Pharmaceutical Industry Ron S. Kenett KPA Ltd., Raanana, Israel and University of Torino, Torino, Italy Past President of ENBIS, the European Network for Business and Industrial Statistics

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3 Current Trends

4 *Nasr, M., "Quality by Design (QbD) A Modern System Approach to Pharmaceutical Development and Manufacturing FDA Perspective", FDA Quality Initiatives Workshop, Maryland, USA, , KPA Ltd., all rights reserved Quality by Design*

5 What is QbD? Reduced Product Variability Process Variability

6 Scope of Recent Guidances Product Design Process Design Manufacturing Process Monitoring/ Continuous Verification ICH Q8/Q8(R) - Pharmaceutical Development PAT Guidance ICH Q9 Quality Risk Management FDA Guidance on Quality Systems (9/06)/ ICH Q10 Pharmaceutical Quality Systems

7 The Quality Ladder* Quality by Design 4 Design of Experiments Process Improvement 3 Statistical Process Control Inspection 2 Sampling Fire Fighting 1 Data Accumulation *Modern Industrial Statistics: Design and Control of Quality and reliability, Kenett and Zacks, Duxbury Press, 1998

8 The Design of Experiments Strategy Process knowledge Process Confidence

9 Process Optimization Where should we operate?

10 Simulation Experiments Design Factors Noise Factors Simulator Code Implementation Issues: Algorithm Convergence criteria Discretization intervals Output

11 The VisiMix Experiment

12 VisiMix Mixing simulation and calculation software 2008, KPA Ltd., all rights reserved Mathematical modeling of mixing phenomena. Calculation of average and local characteristics of mixing flow and distribution of concentration. Simulation and calculation of real non perfect mixing.

13 Path of Steepest Ascent! " " " #$% &% #'$ %() *+&%%% # # " '%% &% #'$ %() #%$%%% * * " #$% "", #'$ %() ""#%%%% & & " '%% "", #'$ %(),"%%%% $ $ " #$% &% - #'$ %() ",#%%%,, " '%% &% - #'$ %() )"%%% + + " #$% "", - #'$ %() $$&%%% ) ) " '%% "", - #'$ %() #+$%%% ' ' " #$% &% *+% %() #&&%%% "% "% " '%% &% *+% %() &$&%%% "" "" " #$% "", *+% %() #$"%%%% "# "# " '%% "", *+% %() "**%%%% "* "* " #$% &% - *+% %() *#$%%% "& "& " '%% &% - *+% %() "+)%%% "$ "$ " #$% "", - *+% %() "##%%%% ", ", " '%% "", - *+% %() +$$%%% "+ "+ " #$% &% #'$ "(% &+"%%% ") ") " '%% &% #'$ "(% #$+%%% "' "' " #$% "", #'$ "(% "&"%%%% #% #% " '%% "", #'$ "(% +$*%%% #" #" " #$% &% - #'$ "(% #%*%%% ## ## " '%% &% - #'$ "(% "%"%%% #* #* " #$% "", - #'$ "(%,%&%%% #& #& " '%% "", - #'$ "(% *&&%%% #$ #$ " #$% &% *+% "(% "%,%%%% #, #, " '%% &% *+% "(% $,)%%% #+ #+ " #$% "", *+% "(% *",%%%% #) #) " '%% "", *+% "(% ",,%%%% #' #' " #$% &% - *+% "(% &&)%%% *% *% " '%% &% - *+% "(% ##*%%% *" *" " #$% "", - *+% "(% "$*%%%% *# *# " '%% "", - *+% "(% +$$%%%

14 Path of Steepest Ascent Normal Probability Plot of the Effects (response is Reynolds, Alpha =.10) 2 1 BD D B A: Vol B: RPM C: Imp D: Visc E: Dens Normal Score 0-1 A BC AB -2 C Effect

15 Path of Steepest Ascent Pareto Chart of the Effects (response is Reynolds, Alpha =.10, only 30 largest effects shown) B D C A BD BC AB E CD AD AC ABD ABC BCD CE DE AE ADE ABCD CDE ACE ABCDE BE ACD ABCE ACDE BCDE ABDE BDE BCE A: Vol B: RPM C: Imp D: Visc E: Dens

16 Path of Steepest Ascent!!!" #$%& &&%&.. /!0 1. /./ /+&)#$%,$')"""(*&%(%%% 2&#+)+$2#"*'*+,$')"2*(#&%(%%* )#+#$%&"*,#$,$')",(#+%(%%% 2$#,+$%2#,**+$,$')"2*(''%(%%% $$,%%%#+)%%%,$')"&(#"%(%%% "',)+$')&*),$')""(&'%("&)

17 Path of Steepest Ascent!!!" #$%& &&%&. /! / /2*%+*"*, 2,$)(#,' "%))&(' 2#,**+$ +&"*(** ')&*+$

18 Path of Steepest Ascent Surface Plot of Reynolds Reynolds RPM Visc Hold values: Vol:-1.0 Imp:-1.0

19 Path of Steepest Ascent Surface Plot of Reynolds Reynolds Vol Visc Hold values: RPM:-1.0 Imp:-1.0

20 Path of Steepest Ascent Surface Plot of Reynolds Reynolds Vol RPM Hold values: Imp:-1.0 Visc:-1.0

21 Path of Steepest Ascent / /+&)#$% 2#"*'*+ &"*,#$ #+)%%% Yˆ * x * x * 2 * x * Vol x R R * 2 RPM x R R * 3 Visc x R R

22 Path of Steepest Ascent Yˆ * x * x * x * 3 R * * * Yˆ * ,290, ,832, ,002, ,764,543

23 The DryLab Experiment

24 Analytic Methods Development DryLab simplifies and speeds the process of developing good chromatographic separations or methods by allowing to model changes in separation conditions using a personal computer.

25 Chromatogram with true signal at 4.9 min. 0,030 Intensity (AU) 0,025 0,020 0,015 0,010 0,005 0,000 4,87 8,13 11,66 12,03 16,91, Propionate de fluticasone 28,64-0,005-0, Retention Time (min)

26 Chromatogram with false negative signal at 4.9 min. Intensity (AU) 0,030 0,025 0,020 0,015 0,010 0,005 0,000 8,19 12,07 12,55, Impurete 3 13,89 16,95, Propionate de fluticasone 18,65, SS-M thyl -0,005-0, Retention Time (min)

27 Chromatogram with false positive signal at 11 min. 10 PDA-246nm T=0 Specificity solution ( 3IMQMN0E002 +~0.15% of impurities) 04A04.dat Retention Time 10 mau mau Typical chromatogram of related substances mau Minutes UV nm System specificity (3IMQMN0E002+~0.15%imp's) Retention Time mau Chromatogram of related substances with False-positive Signal (peak at 11 min) Minutes

28 Simulated Chromatograms

29 Simulation Experiments Results Norm. VWD1 A, Wavelength=262 nm ( \ D) min VWD1 A, Wavelength=262 nm ( \ D) Norm Run Order n-hexane/etoh [v/v] DEA[ml] T[ C] ml 0.5ml 15 C 15 C VWD1 A, Wavelength=262 nm ( \ D) Norm min ml 0.5ml 40 C 40 C min Resolution Norm VWD1 A, Wavelength=262 nm ( \ D) min

30 Simulation Experiments Analysis # Mobile Phase T[ C] RT[min] Isomer #1 RT[min] Isomer #2 Resolution 1 800ml n-hexane 200ml EtOH 2ml Diethylamine(DEA) 15 C RT = Tailing=3.0 Plates=2239 RT = Tailing=2.8 Plates= ml n-hexane 150ml EtOH 0.5ml Diethylamine(DEA) 15 C RT = Tailing=3.6 Plates=2938 RT = Tailing=3.3 Plates= ml n-hexane 150ml EtOH 2ml Diethylamine(DEA) 40 C RT = Tailing=3.0 Plates=2867 RT = Tailing=3.4 Plates= ml n-hexane 200ml EtOH 0.5ml Diethylamine(DEA) 40 C RT = Tailing=3.3 Plates=2236 RT = Tailing=3.7 Plates=

31 Simulation Experiments Analysis Interaction Plot - Data Means for Res V 4 3 Main Effects Plot - Data Means for Res n-hexane/etoh [v/v] Diethylamine(DEA) V T DEA 4.5 T[ C] T 4 3 Res DEA

32 The DynoChem Experiment

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34 The DynoChem Simulation Experiment M. Ben Tolila, R. Kenett, S. Malca, R. Novoa, M. Hasson, B. Yoskovich, 2006, "Scale Up Optimization Using Simulation Experiments" Process Development Symposium: Learn from the Past, Plan for the Future, The American Institute of Chemical Engineers (AIChE), Palm Springs California, June 11-16, 2006.

35 Scale Up Methodology Scale up of chemical processes is a unique chalange facing the chemical and pharmaceutical industry. We face this chalange by applying modern statistical methodology to simulation experiments using the Dynochem software. Using the simulation platform, a statistically designed simulation experiment is designed using Minitab statistical package and optimization is carried using the TITOSIM software. The main outcome is determining the conditions of full scale production. Because scale up is very complex enterprise it needs to be faced by an interdisciplinary team work were technical' chemical process, chemistry, mathematical statistics' computation and others knowledge are represented together.

36 Scale Up Methodology

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38 DynoChem Chemical dynamic simulation software 2008, KPA Ltd., all rights reserved DynoChem applications: Fitting if chemical reaction models. Prediction of scale-up conditions. Optimization of laboratory and production results. Equipment characterization. Shows effects of scale dependent physical phenomena (mixing, heat transfer, mass transfer). Dynochem can be used for simulation of reactions performed in homogenous environment. When mixing is not ideal and the solution is not homogenous VisiMix is used for finding the required mixing conditions.

39 The TITOSIM Project: TIme To market reduction via Statistical Information Management Project No. : GRD /3/ 3014 / / /

40 GRD Gain Knowledge Build Confidence Bates, R., Kenett R., Steinberg D. and Wynn, H. (2006), Achieving Robust Design from Computer Simulations, Quality Technology and Quantitative Management, Vol. 3, No. 2, pp

41 The TEMO production Process 1. Crude TEMO production - The crude production step contains two main operations the reaction and the precipitation. 2. Crystallization This is the main purification step of the process. The reaction is described by these equations:

42 Optimal reaction time yields maximum amount of TEMO and minimum amount of impurities (maximum yield).

43 Model Fitting, Optimization and Simulation 2008, KPA Ltd., all rights reserved

44 Model Fitting, Optimization and Simulation 2008, KPA Ltd., all rights reserved Where: K Reaction Constant (m3/mol.s) and (1/s) E a Reaction Activation Energy (kj/mol) K La Mass transfer coefficient (1/s)

45 Optimization Experiments 8 TEMO batches were produced at RC1 scale at the production conditions set according to the DynoChem simulation and optimization results. The required impurities level is N.M.T 0.15% (for each impurity) at the final product. Impurity levels at production: TEMO <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 CYANO < < <0.03 < < < IMAM 0.10 < <0.03 <0.03 < <0.03 <0.03 <0.03 TMA F F F F F F F F At the end of crude step the impurity levels are higher then spec We can t skip crystallization

46 DATA RESULTS Experiment Stirrer Temp. EOR Yield TEMO TMA CYANO IMAM Nº rpm 0 C h % mmol % % % The experimental array (Simulation experiments)

47 Results

48 Process parameters vs. constraints Case Target Function Yield EOR [hr] Stirrer [rpm] TEMP [ 0 C] A B C D High demand to the product and the reactor. Yield max, EOR8 The price of the product equals 10 times the value of reactor availability. (10yield-EOR)max High demand of the product with low availability of reactors. One hour of available reactor equals 10 times yield. (1yield-10EOR)max High availability of reactors, High cost of impurity purification. (10yield-10IMAM-1EOR)max

49 Non ideal stirring non homogeneity When DynoChem simulation does not match experimental data we should suspect a stirring problem and non homogeneity conditions in the reaction solution. VisiMix software is used in order to find the required stirring characteristics. New conditions are applied on experiments before fitting a model at DynoChem software. For example: The product LETL is produced at a solid liquid reaction. The main reaction at this process is: POCA reagent properties: Solid High particle size: mean=735m High density: 2300 kg/m 3 BBCM + TA + POCA LTRZ suspension must be achieved in order to fit a DynoChem model to the reaction. VisiMix was used for suspension calculation.

50 Non ideal stirring non homogeneity Before performance of scale up experiments VisiMix simulation was used to check suspension at different Mini Pilot Reactors: Reactor Volume, L RPM 500 (Max) (Max) 150 (Max) Main Characteristic Liquid Solid Mixing Solid suspension quality Complete suspension is questionable. Partial settling of solid phase may occur. Complete suspension is expected. Complete suspension is expected. Complete suspension is questionable. Partial settling of solid phase may occur. Max. degree of non uniformity of solid distribution AXIAL, % RADIAL, % Not all Mini Pilot reactor are capable of full suspension of POCA.

51 Financial aspects

52 Adjusting the EOR time according to the financial optimization saves about 4% of the material.

53 For more on DoE Kenett R. and Zacks, S. Modern Industrial Statistics: Design and Control of Quality and Reliability, Duxbury Press, San Francisco, nd edition 2003, Chinese edition 2004.

54 Thank You